1. Technical Field
The present invention relates to a parallel MR imaging method for radial trajectory, and more particularly to a parallel MR imaging method for radial trajectory, which can reconstruct a high-resolution image using a new parallel imaging method that can be used when radially sampling data to acquire an MR image.
2. Related Art
A parallel imaging (PI) method has been frequently used as a method for shortening an image acquisition time in the magnetic resonance imaging (MRI) field. The PI technique is a method for acquiring data using multi-channel coils, that is, several coils, when acquiring the data in MRI. According to this technique, data, the amount of which is smaller than that when the data is obtained using only one coil, is acquired, and then an image is obtained using coil information.
As PI methods in the related art, a radial generalized autocalibrating partially parallel acquisition (GRAPPA) method and an iterative sensitivity encoding (SENSE) method are known.
First, the radial GRAPPA method includes the following processing steps: (a) acquiring, from an MR imaging device, data g to be used for reconstruction and data ACS line to be used to obtain reconstruction coefficients, (b) obtaining the reconstruction coefficients c from a part of g and ACS line obtained in the step (a), (c) predicting unknown data g′ for each coil using g and c obtained in (a, b), and (d) reconstructing g′ obtained in (c) as a final image.
The radial GRAPPA method has the advantages that image reconstruction becomes possible even without acquiring sensitivity information of the coils in advance. Further, the radial GRAPPA method has the advantages that it requires a small amount of computation and an iterative reconstruction method is not required to be used. However, this method has a problem that the quality of an image is degraded due to errors of assumption that is used in a process of extending and applying the GRAPPA technique that is the basis of the present method to radial coordinate system sampling. That is, due to inaccuracy of the reconstruction coefficients obtained in (b), errors occur in a resultant image that is finally reconstructed. Since a detailed algorithm for this is described in “M. A. Griswold at al., Direct Parallel Imaging Reconstruction of Radially Sampled Data Using GRAPPA with Relative Shifts ISMRM 2003”, the detailed explanation thereof will be omitted.
Next, the iterative SENSE method includes the following processing the steps: (1) calculating sensitivity information S from data pre-acquired through an MR imaging device, (2) obtaining an initial image f using data g acquired through the MR imaging device and S calculated in the step (1), (3) correcting an image through a conjugate gradient technique using f obtained in the step (2), and (4) replacing an image f′ corrected through the step (3) by the initial image f in the step (2) and repeatedly performing the steps (2) and (3).
The above-described iterative SENSE method is a technique to repeatedly reconstruct an image, and reconstructs the image using the sensitivity information. The sensitivity information is information having brightness information of several coils that are used in MRI, and can be obtained with reference to the contents described in “Pruessmann K P, Weiger M, Scheidegger M B, Boesiger P. Coil sensitivity encoding for fast MRI. In: Proceedings of the ISMRM 6th Annual Meeting, Sydney, 1998. p 579.”
Further, a detailed algorithm of the iterative SENSE is described in a paper “Klaas P. Pruessmann et al., Advances in Sensitivity Encoding With Arbitrary k-Space Trajectories Magn Reson Med 46:638-651 (2001).”
The iterative SENSE method has the advantage that it can reconstruct a relatively high-resolution image, but has the disadvantage that the error of the image diverges as the number of repetitions for reconstruction is increased in the case where noise occurs in the acquired data due to the characteristics of the conjugate gradient technique used in the above-mentioned step (3).
That is, the conjugate gradient technique used in the step (3) is a method that is frequently used when obtaining a desired solution using an inverse transform. According to this method, a direction in which it is repeatedly traced to reach an optimal solution, starting from a certain value. In this case, the conjugate is used to obtain the direction, and since the direction is unable to be appropriately obtained even if only a small amount of noise exists when the algorithm is actually applied, the resultant value gets far from the optimal solution as the number of repetitions is increased. Because of this, the iterative SENSE method that obtains the solution using the conjugate technique is also sensitive to the noise.